@ARTICLE{Sultana_Mst._Nazma_Hybrid_2021, author={Sultana, Mst. Nazma and Dhar, Nikhil Ranjan}, volume={vol. 68}, number={No 1}, journal={Archive of Mechanical Engineering}, pages={23-49}, howpublished={online}, year={2021}, publisher={Polish Academy of Sciences, Committee on Machine Building}, abstract={The objective of the present study is to optimize multiple process parameters in turning for achieving minimum chip-tool interface temperature, surface roughness and specific cutting energy by using numerical models. The proposed optimization models are offline conventional methods, namely hybrid Taguchi-GRA-PCA and Taguchi integrated modified weighted TOPSIS. For evaluating the effects of input process parameters both models use ANOVA as a supplementary tool. Moreover, simple linear regression analysis has been performed for establishing mathematical relationship between input factors and responses. A total of eighteen experiments have been conducted in dry and cryogenic cooling conditions based on Taguchi L18 orthogonal array. The optimization results achieved by hybrid Taguchi-GRA-PCA and modified weighted TOPSIS manifest that turning at a cutting speed of 144 m/min and a feed rate of 0.16 mm/rev in cryogenic cooling condition optimizes the multi-responses concurrently. The prediction accuracy of the modified weighted TOPSIS method is found better than hybrid Taguchi-GRA-PCA using regression analysis.}, type={Article}, type={Artykuł /Article}, title={Hybrid GRA-PCA and modified weighted TOPSIS coupled with Taguchi for multi-response process parameter optimization in turning AISI 1040 steel}, URL={http://journals.pan.pl/Content/115039/PDF/AME_2021_131707.pdf}, doi={10.24425/ame.2020.131707}, keywords={grey relational analysis, principal component analysis, Taguchi method, analysis of variance, cryogenic cooling}, }